Smartphones or in general handhelds commonly used for indoor localization purposes are not a viable option in places where people do not carry them all the time - for example, home and office. Alternatively, wearable devices can partially solve this problem but have many limitations with respect to power supply, processing capability, and availability of sensors. These issues prevent the adoption of many common handheld localization solutions. In this work, we present PErvasive Localization Engine (PELE), a distributed localization system that uses wearable and handheld jointly to address the above drawbacks. Using only magnetometer, accelerometer, and Bluetooth radio, localization is performed by means of a particle filter. In addition, a dynamic handoff mechanism is presented, which uses the wearable only when it is necessary, thus reducing energy consumption on the wearable without affecting the desired localization accuracy. Evaluating the system with ten participants, we achieve a localization accuracy of 90.31 % in an indoor environment spanning about 320 m2.@en

The recent spate of natural disasters such as earthquakes and floods destroyed buildings and caused loss of lives. Many times, the loss of life is attributed to slow response and not being able to reach the survivors. In such scenarios, the staggering number of deaths in the aftermath of a disaster can be reduced if information about survivors under debris is available to first responders and rescue workers. Large-scale destruction of roads and other communication infrastructure makes it hard to deploy advanced technologies for life detection. We explore the possibility of using low-cost, low-power, short-range communication technologies to assist rescue personnel in locating life under debris. We have designed and prototyped a thermopile-based sensor and communication device that provides information about the presence of survivors. The system weighs under 20 gm and costs US $30 per unit. The device can easily be fitted on battery-powered toy bugs and robots that can autonomously maneuver under the debris. We have proposed three simple algorithms, which together detect humans with 100% to 88% accuracy for 0.5 to 4.5 m range with fewer false alarms. Our evaluation shows that the detection is robust enough under several harsh ambient conditions, temperature ranges as well and partial exposure of the human body.@en